




import math
import time

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
						import torch_xla.core.xla_model as xm
						import torch_xla.debug.metrics as met



class    lowercase_		(_UpperCAmelCase ):



						def __init__(			self	:   Optional[Any]					,							*snake_case__	:   Dict					,							snake_case__	:   Optional[Any]=None					,							snake_case__	:   List[str]=None					,							**snake_case__	:   Any  ):

								"""simple docstring"""

								super().__init__(*lowerCAmelCase__					,							**lowerCAmelCase__  )
								SCREAMING_SNAKE_CASE_					      =							eval_examples
								SCREAMING_SNAKE_CASE_					      =							post_process_function



						def 	__a					(			self	:   Union[str, Any]					,							snake_case__	:   Optional[int]=None					,							snake_case__	:   Optional[Any]=None					,							snake_case__	:   Tuple=None					,							snake_case__	:   Any = "eval"  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.eval_dataset if eval_dataset is None else eval_dataset
								SCREAMING_SNAKE_CASE_					      =							self.get_eval_dataloader(lowerCAmelCase__  )
								SCREAMING_SNAKE_CASE_					      =							self.eval_examples if eval_examples is None else eval_examples

								# Temporarily disable metric computation, we will do it in the loop here.
								SCREAMING_SNAKE_CASE_					      =							self.compute_metrics
								SCREAMING_SNAKE_CASE_					      =							None
								SCREAMING_SNAKE_CASE_					      =							self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
								SCREAMING_SNAKE_CASE_					      =							time.time()
								try:
										SCREAMING_SNAKE_CASE_					      =							eval_loop(
										    lowerCAmelCase__					,							description='Evaluation'					,							prediction_loss_only=True if compute_metrics is None else None					,							ignore_keys=lowerCAmelCase__					,							metric_key_prefix=lowerCAmelCase__					,							)
								finally:
										SCREAMING_SNAKE_CASE_					      =							compute_metrics
								SCREAMING_SNAKE_CASE_					      =							self.args.eval_batch_size * self.args.world_size
								if f'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
										start_time += output.metrics[f'''{metric_key_prefix}_jit_compilation_time''']
								output.metrics.update(
								    speed_metrics(
								        lowerCAmelCase__					,							lowerCAmelCase__					,							num_samples=output.num_samples					,							num_steps=math.ceil(output.num_samples / total_batch_size  )					,							)  )
								if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
										# Only the main node write the results by default
										SCREAMING_SNAKE_CASE_					      =							self.post_process_function(lowerCAmelCase__					,							lowerCAmelCase__					,							output.predictions  )
										SCREAMING_SNAKE_CASE_					      =							self.compute_metrics(lowerCAmelCase__  )

										# Prefix all keys with metric_key_prefix + '_'
										for key in list(metrics.keys()  ):
												if not key.startswith(f'''{metric_key_prefix}_'''  ):
														SCREAMING_SNAKE_CASE_					      =							metrics.pop(lowerCAmelCase__  )
										metrics.update(output.metrics  )
								else:
										SCREAMING_SNAKE_CASE_					      =							output.metrics

								if self.args.should_log:
										# Only the main node log the results by default
										self.log(lowerCAmelCase__  )

								if self.args.tpu_metrics_debug or self.args.debug:
										# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
										xm.master_print(met.metrics_report()  )

								SCREAMING_SNAKE_CASE_					      =							self.callback_handler.on_evaluate(self.args					,							self.state					,							self.control					,							lowerCAmelCase__  )
								return metrics






						def 	__a					(			self	:   List[str]					,							snake_case__	:   int					,							snake_case__	:   int					,							snake_case__	:   Any=None					,							snake_case__	:   Any = "test"  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.get_test_dataloader(lowerCAmelCase__  )

								# Temporarily disable metric computation, we will do it in the loop here.
								SCREAMING_SNAKE_CASE_					      =							self.compute_metrics
								SCREAMING_SNAKE_CASE_					      =							None
								SCREAMING_SNAKE_CASE_					      =							self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
								SCREAMING_SNAKE_CASE_					      =							time.time()
								try:
										SCREAMING_SNAKE_CASE_					      =							eval_loop(
										    lowerCAmelCase__					,							description='Prediction'					,							prediction_loss_only=True if compute_metrics is None else None					,							ignore_keys=lowerCAmelCase__					,							metric_key_prefix=lowerCAmelCase__					,							)
								finally:
										SCREAMING_SNAKE_CASE_					      =							compute_metrics
								SCREAMING_SNAKE_CASE_					      =							self.args.eval_batch_size * self.args.world_size
								if f'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
										start_time += output.metrics[f'''{metric_key_prefix}_jit_compilation_time''']
								output.metrics.update(
								    speed_metrics(
								        lowerCAmelCase__					,							lowerCAmelCase__					,							num_samples=output.num_samples					,							num_steps=math.ceil(output.num_samples / total_batch_size  )					,							)  )

								if self.post_process_function is None or self.compute_metrics is None:
										return output

								SCREAMING_SNAKE_CASE_					      =							self.post_process_function(lowerCAmelCase__					,							lowerCAmelCase__					,							output.predictions					,							'predict'  )
								SCREAMING_SNAKE_CASE_					      =							self.compute_metrics(lowerCAmelCase__  )

								# Prefix all keys with metric_key_prefix + '_'
								for key in list(metrics.keys()  ):
										if not key.startswith(f'''{metric_key_prefix}_'''  ):
												SCREAMING_SNAKE_CASE_					      =							metrics.pop(lowerCAmelCase__  )
								metrics.update(output.metrics  )
								return PredictionOutput(predictions=predictions.predictions					,							label_ids=predictions.label_ids					,							metrics=lowerCAmelCase__  )
from argparse import ArgumentParser

from .env import EnvironmentCommand





def 							UpperCAmelCase		(       ):







				"""simple docstring"""



				__lowercase					     =				ArgumentParser('''Diffusers CLI tool'''				,     usage='''diffusers-cli <command> [<args>]'''				)
				__lowercase					     =				parser.add_subparsers(help='''diffusers-cli command helpers'''				)

				# Register commands
				EnvironmentCommand.register_subcommand(lowercase				)

				# Let's go
				__lowercase					     =				parser.parse_args()

				if not hasattr(lowercase				,     '''func'''				):
								parser.print_help()
								exit(1				)

				# Run
				__lowercase					     =				args.func(lowercase				)
				service.run()


if __name__ == "__main__":
		main()